Operator supervisory control of unmanned-vehicle (UV) teams has become a major area of research and development due to advancements in technology seeking to relieve human operators of low-level tasking and a focus on network-centric operations, which requires higher levels of human reasoning and judgment.
Complex problems similar to those that present themselves through concepts such as network-centric operations and future combat systems require a transition from the architecture of today, where vehicles are supervised on an individual basis, to an architecture where operators integrate information retrieved from multiple platforms. Allowing for systems where multiple vehicles can be supervised by a single operator simultaneously facilitates such operational architectures.
Increasing use of automation in unmanned vehicle systems has shifted human operator responsibility from manually controlling vehicles to managing vehicles at the supervisory control level. At the supervisory control level, implementation details of higher-level tasking initiated by the human are delegated to the automation onboard these vehicles. The reduced workload afforded by supervisory control has several implications. One such implication is an increase in operator idle time, which can be used as a force multiplier that allows operators to supervise multiple vehicles simultaneously, hence inverting the current many-to-one ratio of operators to vehicles.
There are several driving forces, such as network centric operations and the associated widely diverse set of mission possibilities that require interoperability among UVs of varying attributes. Therefore, heterogeneity in vehicle capabilities and tasks is likely to exist in future UV systems. This will lead to a large design space for these systems, which will cause design validation to require lengthy and expensive human-in-the-loop experimentation.
To maximize UV control capabilities, it is desirable to determine a number of unmanned vehicles that can be controlled by a single human controller. Unfortunately, the process of performing experimentation to determine a number of unmanned vehicles that can be controlled by a single user can be very costly due to items such as, but not limited to, setup costs and personnel required. In addition, different human controllers may have different control capabilities, thereby requiring many experiments with different human controllers, which again adds to design validation expense.
In determining human-UV performance, it is important to note that operator situational awareness can significantly influence human behavior and hence, human-UV system performance. A lack of situational awareness on the part of the operator results in increased time for the operator to notice the needs of the system, which then results in increased wait times for the system. Thus, one negative side effect of low situational awareness is additional vehicle wait times due to loss of situational awareness. It has been shown that wait times due to loss of situational awareness can account for the largest part of vehicle wait time, and significantly reduces the overall number of vehicles that a single operator can control.
Therefore, it is desirable to determine a maximum number of heterogeneous unmanned vehicles that can be controlled by a user in an efficient and inexpensive manner, while capturing the effects of wait times due to loss of situational awareness. In addition, it is desirable to determine the efficiency of human-UV interaction at different vehicle team sizes, as well as at different settings for other system variables, such as, level of autonomy and level of heterogeneity of the vehicle team.